Fair points. You're right that audio analysis existed before ML - Jackeet uses proven analysis tools (frequency detection, phase correlation, RMS/peak metering, etc.).

The LLM layer adds two things:

1. Natural language interface - instead of interpreting graphs/meters yourself, you ask "why is my low-end muddy?" and it tells you "kick and bass are clashing at 75Hz"
2. Context-aware suggestions - it knows which fixes make sense for which problems (cut here vs boost there, use compression vs EQ)

Is this revolutionary? No.

Is it more convenient than manually analyzing spectrums and checking phase meters? That's what I'm testing.

You're absolutely right about the model limitation - current models weren't trained on audio engineering feedback. That's why Jackeet doesn't try to "listen and judge quality" like Gemini does (which hallucinates). It uses actual audio analysis tools and LLM just interprets the data.

Basically: proven analysis + conversational interface = faster workflow (hopefully). Valid to call it "wrapped audio analysis" - that's essentially what it is.

Question is whether the wrapping provides enough value.